Applying machine learning to the flagellar motor for biosensing

Abstract

Escherichia coli detects and follows chemical gradients in its environment in a process known as chemotaxis. The performance of chemotaxis approaches fundamental biosensor speed and sensitivity limits, but there have been relatively few attempts to incorporate the response into a functional biosensor. Toward that end, we have developed software to process digital microscope images of a large number of tethered E. coli responding to different chemical perturbations. Upwards of fifty cells can be recorded in one experiment, allowing for rapid labeling of the chemotactic responses of multiple cells. After we collected hundreds of wild-type chemotactic E. coli motor responses to dilutions of aspartate and leucine, we trained a support vector classifier (SVC) to estimate the order of magnitude of aspartate concentration between 0M, 100nM, and 1μM with a single cell classification subset accuracy of 69%. We trained another SVC to differentiate between aspartate and leucine with a single cell classification subset accuracy of 83%. Using a majority-vote method on a bacterial population of size N, estimates have 95% confidence for N = 27 bacteria for concentration detection and N = 9 bacteria for chemical differentiation. These methods are a step towards adaptable chemotaxis-based biosensing.